Machine Learning Engineers sit at the intersection of Data Science and Software Engineering. They leverage different programming frameworks and extensive data tools to ensure the raw data gathered from the data pipelines are redefined as Data Science models are available to scale as needed.
This article will provide you with a roadmap on how to become a Machine Learning Engineer.
In this guide, we will discuss:
- Who Is a Machine Learning Engineer?
- What Does a Machine Learning Engineer Do?
- Skills You Need to Become a Machine Learning (ML) Engineer?
- Roles and Responsibilities of a Machine Learning (ML) Engineer.
- Salaries & Trends Of Machine Learning (ML) Engineer.
- Who Hires Machine Learning Engineers?
Who Is a Machine Learning Engineer?
Machine Learning (ML) Engineers are experienced programmers who develop machines and computer systems to learn and apply their knowledge without any specific direction. So they’re just enthusiastic computer programmers, but the focus goes beyond specifically programming machines to perform tasks. ML Engineers develop programs that enable machines to act without being directed by someone to perform that task.
What Does a Machine Learning (ML) Engineer Do?
ML Engineers are creators of the algorithms that allow the machine to find patterns in its own programming data, teaching it to understand commands and even think for itself the Artificial Intelligence (AI) seen in automatic vacuums and self-driving cars is the thought children of these Engineers researching new technologies and implementing them in Machine Learning.
Programs are one of the many tasks that a Machine Learning Engineer does. Finding the best design and hardware to use when building the robot computer, developing tangible prototypes to show stakeholders, and putting the machines through various rigorous tests to ensure they function as planned.
Skills Required To Become a Machine Learning (ML) Engineer
The first most important skill is programming skill. They include data structure algorithms, computability and complexity, and even computer architecture. You must have enough knowledge to apply, implement, adapt or address them. Best Programming practice, solving problems, coding, and competitions are great ways to hone your skills.
So some of the programming languages that you must be familiar with are:
- R language
- R language by using the R language, we can be developing statistical software and data analysis.
- Python is a programming language that allows you to quickly create, analyze, and organize large chunks of data.
- Java is a platform-independent language that helps in the data description.
More Important Skills For ML Engineer are:
- Probability and Statistics
A ritual characterization probability and techniques derived from this are the heart of many Machine Learning algorithms.
These are a means to deal with uncertainty in the real world closely related to this field because statistics provide varnish measures like mean, median, variance, etc.
Distributions like uniform normal, binomial, and analysis methods like hypothesis testing are significant for building and validating models from observed data. Many of the Machine Learning algorithms are necessary extensions for statistical modeling procedures.
- Data Modeling and Evaluation
It is simply the process of estimating the underlying structure of a given data set to find valuable patterns like correlations clusters etc., and predicting properties of previously unseen instances. A primary part of this estimation process is evaluating how good a given model is.
Depending on the task at hand, you will need to choose an appropriate accuracy or error measure. Examples include logs, a loss for classification, squared sum, errors for regression, and an evaluation strategy like testing split sequential versus randomized cross-validation.
Iterative learning algorithms often directly utilize resulting errors to tweak the model; examples are backpropagation for neural networks. Hence, understanding these crucial measures is essential even for just applying standard algorithms.
- Applying Machine Learning Algorithms and Libraries
These are critical standard implementations of machine learning. Algorithms are widely available through libraries, packages, and APIs, and some examples are TensorFlow and Sci-Kit learns.
But applying them effectively involves choosing a suitable model like the decision tree, nearest neighbor, neural net, and a learning procedure to fit the data and understand how hyperparameters affect learning is really important.
You need to understand how bias-variance works overfitting and underfitting. The basics of Machine Learning and how you apply them to data science and Machine Learning challenges such as those on Kaggle are a great way to get exposed to different kinds of problems.
- Software Engineering & System Design
To become a Machine Learning (ML) Engineer, you need to understand how Software Engineering and System Design works.
By the end of the day, Machine Learning Engineers make the output of deliverable software. It is often a minimal component that fits into a larger ecosystem of products and services.
You need to understand how these different pieces work together to communicate with them and build appropriate interfaces for your component that others will eventually depend on.
Careful system design is necessary to avoid bottlenecks and allow your algorithms to scale well with increasing data volumes.
Responsibilities of Machine Learning (ML) Engineer
Now that we have discussed the skills needed to become a Machine Learning (ML) Engineer let us look at some of the significant roles and responsibilities.
- Study some Prototypes and transform them into applications.
- Design & Build a Machine Learning System.
- Find appropriate Algorithms and Tools.
- Develop Machine Learning Applications.
- Select the suitable dataset and find the correct Data representation method.
- Run Machine Learning Tests and Experiments.
- Train the systems for top-notch Accuracy.
Salaries & Trends Of a Machine Learning (ML) Engineer
According to the 2019 Indeed report, the US market’s best jobs are Machine Learning Engineers. More interesting is that an ML Engineer’s role recorded a whopping 344% increase since 2015.
The average salary for a Machine Learning Engineer is $112,448. That’s really a considerable amount, right?
Companies That Hires Machine Learning Engineers
The number of opportunities for Machine Learning Engineers is growing fast, which is fantastic because you’ll be trending when you’re a Machine Learning Engineer.
You’ll be paid well by everyone from Apple to Uber, Facebook to Salesforce. These big players are on constant Machine Learning (ML) Engineers hiring spear and paying high salaries.
If Machine Learning (ML) Engineering is your future, then there’s no time like the present to become an expert in the skills and develop the mindset that you’re going to succeed.
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